{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "This notebook can be used to: \n",
    "1. vizualise training data\n",
    "2. vizualise prediction of a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import torch, json\n",
    "import numpy as np\n",
    "import re\n",
    "import torch.nn as nn\n",
    "from main_synthetic import build_model_main\n",
    "from util.slconfig import SLConfig\n",
    "import editdistance\n",
    "from datasets import build_dataset\n",
    "from util.visualizer import COCOVisualizer\n",
    "from util import box_ops\n",
    "import editdistance\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Synthetic data\n",
    "Synthetic data are saved in **/datasets/synthetic_**. We advice to use the following code to / visualize the data augmentation used during training and the bouding boxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_config_path = \"config/Latin.py\" # change the path of the model config file\n",
    "args = SLConfig.fromfile(model_config_path) \n",
    "args.device = 'cuda:0' \n",
    "args.coco_path = \"/comp_robot/cv_public_dataset/COCO2017/\" # the path of coco\n",
    "args.fix_size = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latin\n",
    "change **args.language = 'en'** to specify either french, english, or german. Comment for random characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USING language: en \n"
     ]
    }
   ],
   "source": [
    "args.dataset_file = 'synthetic_line_OCR_general'\n",
    "args.language = 'en' # comment for general\n",
    "args.random_erasing = True\n",
    "\n",
    "dataset_val = build_dataset(image_set='train', args=args) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = np.random.randint(0, len(dataset_val))\n",
    "image, targets = dataset_val[i]\n",
    "boxes = targets['boxes']\n",
    "\n",
    "\n",
    "\n",
    "box_label = [dataset_val.charset[int(item)] for item in targets['labels']] # chinese character are encoded in unicode\n",
    "pred_dict = {\n",
    "'boxes': boxes,\n",
    "'size': torch.tensor([image.shape[-2], image.shape[-1]]),\n",
    "'box_label': box_label,\n",
    "'image_id': 0\n",
    "\n",
    "}\n",
    "vslzr = COCOVisualizer()\n",
    "vslzr.visualize(image, pred_dict,  show_in_console=True,fontsize =8, offset=120,dpi = 150,Chinese=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chinese"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "args.dataset_file = 'HWDB_synth' ## change the dataset file\n",
    "args.random_erasing = True\n",
    "\n",
    "dataset_val = build_dataset(image_set='train', args=args)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = np.random.randint(0, len(dataset_val))\n",
    "image, targets = dataset_val[i]\n",
    "boxes = targets['boxes']\n",
    "\n",
    "\n",
    "\n",
    "box_label = [chr(dataset_val.charset[int(item)]) for item in targets['labels']] # chinese character are encoded in unicode\n",
    "pred_dict = {\n",
    "'boxes': boxes,\n",
    "'size': torch.tensor([image.shape[-2], image.shape[-1]]),\n",
    "'box_label': box_label,\n",
    "'image_id': 0\n",
    "\n",
    "}\n",
    "vslzr = COCOVisualizer()\n",
    "vslzr.visualize(image, pred_dict,  show_in_console=True,fontsize =8, offset=120,dpi = 150,Chinese=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real Datasets\n",
    "Real datasets are expected to be stored in the folder indicated in datasets/config.json. We advice to use the following code to vizualise the random erasing, in particular for a custom dataset. \n",
    "Real datasets have no annotations for the bouding boxes, only lines transcription"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_config_path = \"config/Latin.py\" # change the path of the model config file\n",
    "args = SLConfig.fromfile(model_config_path) \n",
    "args.device = 'cuda:0' \n",
    "args.coco_path = \"/comp_robot/cv_public_dataset/COCO2017/\" # the path of coco\n",
    "args.fix_size = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change args.dataset_file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train\n"
     ]
    }
   ],
   "source": [
    "args.dataset_file = 'HWDB' ## change the dataset file\n",
    "dataset_val = build_dataset(image_set='train', args=args)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "面积全部被太阳能电池覆盖,\u0000可以随时为手机充电。4\u00000\u0000\n"
     ]
    }
   ],
   "source": [
    "i = np.random.randint(0, len(dataset_val))\n",
    "image, targets = dataset_val[i]\n",
    "boxes = targets['boxes']\n",
    "\n",
    "\n",
    "\n",
    "box_label = [dataset_val.charset[int(item)] for item in targets['labels']] # chinese character are encoded in unicode\n",
    "\n",
    "pred_dict = {\n",
    "'boxes': boxes,\n",
    "'size': torch.tensor([image.shape[-2], image.shape[-1]]),\n",
    "'box_label': box_label,\n",
    "'image_id': 0\n",
    "\n",
    "}\n",
    "vslzr = COCOVisualizer()\n",
    "vslzr.visualize(image, pred_dict,  show_in_console=True,fontsize =8, offset=120,dpi = 150,Chinese=True)\n",
    "try:\n",
    "    str_gt = ''.join(box_label)\n",
    "except:\n",
    "    # for HWDB dataset\n",
    "    box_label = [chr(dataset_val.charset[int(item)]) for item in targets['labels']]\n",
    "    str_gt = ''.join(box_label)\n",
    "print(str_gt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize the prediction of a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "args.coco_path = \"/comp_robot/cv_public_dataset/COCO2017/\" # the path of coco\n",
    "args.fix_size = False\n",
    "args = SLConfig.fromfile(model_config_path) \n",
    "device = args.device = 'cuda:0' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the following: **weight** and **dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrained_model = True\n",
    "model_config_path = \"config/Latin_CTC.py\" # change the path of the model config file\n",
    "model_checkpoint_path =  \"/home/rbaena/projects/OCR/n-gram/RIMES_w_w/checkpoint.pth\"\n",
    "args.dataset_file = 'RIMES' # change the dataset file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dataset_val = build_dataset(image_set='test', args=args)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DINO(\n",
       "  (transformer): DeformableTransformer(\n",
       "    (encoder): TransformerEncoder(\n",
       "      (layers): ModuleList(\n",
       "        (0-5): 6 x DeformableTransformerEncoderLayer(\n",
       "          (self_attn): MSDeformAttn(\n",
       "            (sampling_offsets): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (attention_weights): Linear(in_features=256, out_features=128, bias=True)\n",
       "            (value_proj): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (output_proj): Linear(in_features=256, out_features=256, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.0, inplace=False)\n",
       "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
       "          (dropout3): Dropout(p=0.0, inplace=False)\n",
       "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (decoder): TransformerDecoder(\n",
       "      (layers): ModuleList(\n",
       "        (0-5): 6 x DeformableTransformerDecoderLayer(\n",
       "          (cross_attn): MSDeformAttn(\n",
       "            (sampling_offsets): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (attention_weights): Linear(in_features=256, out_features=128, bias=True)\n",
       "            (value_proj): Linear(in_features=256, out_features=256, bias=True)\n",
       "            (output_proj): Linear(in_features=256, out_features=256, bias=True)\n",
       "          )\n",
       "          (dropout1): Dropout(p=0.0, inplace=False)\n",
       "          (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "          (self_attn): MultiheadAttention(\n",
       "            (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n",
       "          )\n",
       "          (dropout2): Dropout(p=0.0, inplace=False)\n",
       "          (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "          (linear1): Linear(in_features=256, out_features=2048, bias=True)\n",
       "          (dropout3): Dropout(p=0.0, inplace=False)\n",
       "          (linear2): Linear(in_features=2048, out_features=256, bias=True)\n",
       "          (dropout4): Dropout(p=0.0, inplace=False)\n",
       "          (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "      (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "      (ref_point_head): MLP(\n",
       "        (layers): ModuleList(\n",
       "          (0): Linear(in_features=512, out_features=256, bias=True)\n",
       "          (1): Linear(in_features=256, out_features=256, bias=True)\n",
       "        )\n",
       "      )\n",
       "      (bbox_embed): ModuleList(\n",
       "        (0-5): 6 x MLP(\n",
       "          (layers): ModuleList(\n",
       "            (0-1): 2 x Linear(in_features=256, out_features=256, bias=True)\n",
       "            (2): Linear(in_features=256, out_features=4, bias=True)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (class_embed): ModuleList(\n",
       "        (0-5): 6 x Linear(in_features=256, out_features=166, bias=True)\n",
       "      )\n",
       "    )\n",
       "    (tgt_embed): Embedding(900, 256)\n",
       "    (enc_output): Linear(in_features=256, out_features=256, bias=True)\n",
       "    (enc_output_norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n",
       "    (enc_out_bbox_embed): MLP(\n",
       "      (layers): ModuleList(\n",
       "        (0-1): 2 x Linear(in_features=256, out_features=256, bias=True)\n",
       "        (2): Linear(in_features=256, out_features=4, bias=True)\n",
       "      )\n",
       "    )\n",
       "    (enc_out_class_embed): Linear(in_features=256, out_features=166, bias=True)\n",
       "  )\n",
       "  (label_enc): Embedding(168, 256)\n",
       "  (input_proj): ModuleList(\n",
       "    (0): Sequential(\n",
       "      (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
       "    )\n",
       "    (1): Sequential(\n",
       "      (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
       "    )\n",
       "    (2): Sequential(\n",
       "      (0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
       "    )\n",
       "    (3): Sequential(\n",
       "      (0): Conv2d(2048, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
       "      (1): GroupNorm(32, 256, eps=1e-05, affine=True)\n",
       "    )\n",
       "  )\n",
       "  (backbone): Joiner(\n",
       "    (0): Backbone(\n",
       "      (body): IntermediateLayerGetter(\n",
       "        (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "        (bn1): FrozenBatchNorm2d()\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "        (layer1): Sequential(\n",
       "          (0): Bottleneck(\n",
       "            (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "            (downsample): Sequential(\n",
       "              (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "              (1): FrozenBatchNorm2d()\n",
       "            )\n",
       "          )\n",
       "          (1): Bottleneck(\n",
       "            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "          (2): Bottleneck(\n",
       "            (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "        )\n",
       "        (layer2): Sequential(\n",
       "          (0): Bottleneck(\n",
       "            (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "            (downsample): Sequential(\n",
       "              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "              (1): FrozenBatchNorm2d()\n",
       "            )\n",
       "          )\n",
       "          (1): Bottleneck(\n",
       "            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "          (2): Bottleneck(\n",
       "            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "          (3): Bottleneck(\n",
       "            (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "        )\n",
       "        (layer3): Sequential(\n",
       "          (0): Bottleneck(\n",
       "            (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "            (downsample): Sequential(\n",
       "              (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "              (1): FrozenBatchNorm2d()\n",
       "            )\n",
       "          )\n",
       "          (1): Bottleneck(\n",
       "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "          (2): Bottleneck(\n",
       "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "          (3): Bottleneck(\n",
       "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "          (4): Bottleneck(\n",
       "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "          (5): Bottleneck(\n",
       "            (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "        )\n",
       "        (layer4): Sequential(\n",
       "          (0): Bottleneck(\n",
       "            (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "            (downsample): Sequential(\n",
       "              (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "              (1): FrozenBatchNorm2d()\n",
       "            )\n",
       "          )\n",
       "          (1): Bottleneck(\n",
       "            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "          (2): Bottleneck(\n",
       "            (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn1): FrozenBatchNorm2d()\n",
       "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn2): FrozenBatchNorm2d()\n",
       "            (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn3): FrozenBatchNorm2d()\n",
       "            (relu): ReLU(inplace=True)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (1): PositionEmbeddingSineHW()\n",
       "  )\n",
       "  (bbox_embed): ModuleList(\n",
       "    (0-5): 6 x MLP(\n",
       "      (layers): ModuleList(\n",
       "        (0-1): 2 x Linear(in_features=256, out_features=256, bias=True)\n",
       "        (2): Linear(in_features=256, out_features=4, bias=True)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (class_embed): ModuleList(\n",
       "    (0-5): 6 x Linear(in_features=256, out_features=166, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charset = dataset_val.charset\n",
    "model, criterion, postprocessors = build_model_main(args)\n",
    "\n",
    "checkpoint = torch.load(model_checkpoint_path, map_location='cpu')\n",
    "try:\n",
    "     model.load_state_dict(checkpoint['model'])\n",
    "except:\n",
    "    features_dim = model.class_embed[0].weight.data.shape[1]\n",
    "    new_charset_size = len(charset)\n",
    "    new_class_embed = nn.Linear(features_dim,new_charset_size ,)\n",
    "    new_decoder_class_embed = nn.Linear(features_dim,new_charset_size ,)\n",
    "    new_enc_out_class_embed = nn.Linear(features_dim,new_charset_size ,)\n",
    "    if model.dec_pred_class_embed_share:\n",
    "        class_embed_layerlist = [new_class_embed for i in range(model.transformer.num_decoder_layers)]\n",
    "\n",
    "        new_class_embed = nn.ModuleList(class_embed_layerlist)\n",
    "        model.class_embed = new_class_embed.to(device)\n",
    "        model.transformer.decoder.class_embed = new_decoder_class_embed.to(device)\n",
    "        model.transformer.enc_out_class_embed = new_enc_out_class_embed.to(device)\n",
    "        try:\n",
    "            checkpoint = torch.load(model_checkpoint_path, map_location='cpu')\n",
    "            model.load_state_dict(checkpoint['model'])\n",
    "        except:\n",
    "            model.label_enc = nn.Embedding(len(dataset_val.charset)+1,features_dim ).to(device)\n",
    "            checkpoint = torch.load(model_checkpoint_path, map_location='cpu')\n",
    "            model.load_state_dict(checkpoint['model'])\n",
    "\n",
    "model.eval()\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 7680x5760 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pt:  Sincères salutations.\n",
      "gt:  Sincères salutations.\n",
      "CER:  0.0\n"
     ]
    }
   ],
   "source": [
    "i = 203\n",
    "image, targets = dataset_val[i]\n",
    "\n",
    "with torch.no_grad():\n",
    "    model.eval()\n",
    "    output = model.to(device)(image[None].to(device))\n",
    "\n",
    "    postprocessors['bbox'].num_select = 900\n",
    "    postprocessors['bbox'].nms_iou_threshold = 0.3\n",
    "    output = postprocessors['bbox'](output, torch.Tensor([[1.0, 1.0]]).cuda())[0]\n",
    "\n",
    "    thershold = 0.3#\n",
    "\n",
    "    scores = output['scores']\n",
    "    labels = output['labels']\n",
    "\n",
    "    \n",
    "    boxes = box_ops.box_xyxy_to_cxcywh(output['boxes'])\n",
    "    select_mask = scores > thershold\n",
    "    indices_sort = boxes[select_mask][:,0].sort()[1]\n",
    "    box_label = []\n",
    "    try:\n",
    "        try:\n",
    "            box_label = [chr(dataset_val.charset[int(item)]) for item in labels[select_mask][indices_sort]]\n",
    "        except:\n",
    "            box_label = [dataset_val.charset[int(item)] for item in labels[select_mask][indices_sort]]\n",
    "    except:\n",
    "        try:\n",
    "            box_label = [chr(charset[int(item)]) for item in labels[select_mask][indices_sort]]\n",
    "        except:\n",
    "            box_label = [charset[int(item)] for item in labels[select_mask][indices_sort]]\n",
    "\n",
    "    pred_dict = {\n",
    "    'boxes': boxes[select_mask][indices_sort],\n",
    "    'size': torch.tensor([image.shape[-2], image.shape[-1]]),\n",
    "    'box_label': box_label,\n",
    "    'image_id': 0\n",
    "    \n",
    "}\n",
    "    vslzr = COCOVisualizer()\n",
    "    vslzr.visualize(image, pred_dict,  show_in_console=True,fontsize =18, offset=120, savedir ='/home/rbaena/projects/OCR/Copiale.jpg',dpi = 1200,Chinese=True)\n",
    "str_pred = \"\".join(box_label)\n",
    "str_pred = str_pred.replace(\"\",\"\")\n",
    "print('pt: ',str_pred)\n",
    "try:\n",
    "    str_gt = \"\".join([chr(dataset_val.charset[item]) for item in targets['labels']])\n",
    "except:\n",
    "    str_gt = \"\".join([dataset_val.charset[item] for item in targets['labels']])\n",
    "print('gt: ',str_gt)\n",
    "CER = editdistance.eval(str_pred,str_gt)/ len(str_gt)\n",
    "print('CER: ',CER)\n"
   ]
  }
 ],
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